Linear multiply-accumulate (MAC) operations have been the main focus of prior efforts in improving the energy efficiency of neural network inference due to their dominant contribution to energy consumption in traditional models. On the other hand, …

Stochastic computing is a statistical computing scheme that represents data as serial bit streams to greatly reduce hardware complexity. The key trade-off is that processing more bits in the streams yields higher computation accuracy at the cost of …

General matrix multiplication (GEMM) is universal in various applications, such as signal processing, machine learning, and computer vision. Conventional GEMM hardware architectures based on binary computing exhibit low area and energy efficiency as …

For error-resilient applications, such as machine learning and signal processing, a significant improvement in energy efficiency can be achieved by relaxing exactness constraint on output quality. This paper presents a taxonomy of hardware techniques …

From signal processing to emerging deep neural networks, a range of applications exhibit intrinsic error resilience. For such applications, approximate computing opens up new possibilities for energy-efficient computing by producing slightly …

Stochastic Computing (SC) is designed to minimize hardware area and power consumption compared to traditional binary-encoded computation, stemming from the bit-serial data representation and extremely straightforward logic. Though existing Stochastic …

By using stochastic computation, a fully-parallel low-density parity-check (LDPC) decoder can be implemented using a lower wire complexity. In order to enhance the decoder performance, probability tracers, such as up/down counters, are added at each …

Stochastic computation is an excellent approach for low-density parity-check codes decoding. By adding edge memories at each edge in the Tanner graph, fully parallel hardware implementation can be designed with much lower wire complexity. This …

Stochastic decoding can be applied to Low-Density Parity-Check codes in order to achieve high throughput with less area. However, most architectures suffer from large decoding latencies, due to the mechanism of stochastic computation. In this paper, …

As Optical Communication is on the way, conventional LDPC decoders do not work well with the requirement for high throughput over 100 Gb/s. Many new LDPC decoder structures aiming at high throughput have been proposed, such as stochastic decoders, …